Authors : Nahier Aldhafferi, Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji and Abdullah Alqahtani
Abstract: The significance of Relative Cooling Power (RCP) of manganite-based magnetic refrigerant in Magnetic Refrigeration (MR) technology cannot be over-emphasized. Although, MR system overcomes the setbacks of conventional gas compression technology with its better performance, low cost and little or no environmental hazard. However, experimental determination of the refrigerant RCP is subjected to procedures and routines that are not only challenging but also consume appreciable time and other valuable resources. This necessitates for a simple and cost effective modeling technique that preserves the experimental precision and accuracy. Therefore, this research develops Sensitivity-Based Linear Learning Method (SBLLM) of training two-layer feedforward neural network for estimating RCP of manganite-based materials using ionic radii and dopants concentration as inputs to the model. The number of epoch and hidden neurons of the network are optimized using Gravitational Search Algorithm (GSA). The results of the developed GSA-SBLLM Model agree well with the experimentally measured values. The strength and robustness of the developed GSA-SBLLM Model include its ability to incorporate up to four different dopants and their respective concentrations to manganite material for magnetic refrigerant RCP estimation. This ability coupled with the precision of its estimates is of significant impact in magnetic refrigeration enhancement without experimental challenges.
Nahier Aldhafferi, Taoreed O. Owolabi, Kabiru O. Akande, Sunday O. Olatunji and Abdullah Alqahtani, 2018. Development of Hybrid Computational Intelligence Model for Estimating Relative Cooling Power of Manganite-Based Materials for Magnetic Refrigeration Enhancement. Journal of Engineering and Applied Sciences, 13: 1575-1583.